Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning

• URL: http://arxiv.org/abs/2004.03829v2
• Date: Mon, 21 Sep 2020 05:09:59 GMT
• Title: Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning
• Authors: Zhaojiang Lin, Andrea Madotto, Pascale Fung
• Abstract summary: We propose an effective way to fine-tune multiple down-stream generation tasks simultaneously using a single, large pre-trained model. The experiments on five diverse language generation tasks show that by just using an additional 2-3% parameters for each task, our model can maintain or even improve the performance of fine-tuning the whole model.
• Score: 70.81910984985683
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-tuning pre-trained generative language models to down-stream language generation tasks has shown promising results. However, this comes with the cost of having a single, large model for each task, which is not ideal in low-memory/power scenarios (e.g., mobile). In this paper, we propose an effective way to fine-tune multiple down-stream generation tasks simultaneously using a single, large pre-trained model. The experiments on five diverse language generation tasks show that by just using an additional 2-3% parameters for each task, our model can maintain or even improve the performance of fine-tuning the whole model.

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